Some (early) evidence that managers have the highest success rate in using Claude Code for coding.
I have been arguing that management is an AI superpower, as clearly specifying what you want, how to do it & what good looks like is key to using agents. https://www.oneusefulthing.org/p/management-as-ai-superpower
Many users agree that managers match software engineers with Claude Code because delegation and clear specification skills transfer directly to guiding AI agents effectively.
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#184And here is the Anthropic report with the chart: https://cdn.sanity.io/files/4zrzovbb/website/b93c7465925dc052b9102209b29b58f11df4fe55.pdf
Some (early) evidence that managers have the highest success rate in using Claude Code for coding.
I have been arguing that management is an AI superpower, as clearly specifying what you want, how to do it & what good looks like is key to using agents. https://www.oneusefulthing.org/p/management-as-ai-superpower
And this really is early evidence: sample size was excellent and approach to verifying outputs makes sense, but there are lots of unobservable elements that could play a role in relative success rates.
And here is the Anthropic report with the chart: https://cdn.sanity.io/files/4zrzovbb/website/b93c7465925dc052b9102209b29b58f11df4fe55.pdf
I've seen this firsthand. I know engineers who are 1000x better than me at Python, CSS, scripts, frontend — but most haven't had to manage people. And managing people is exactly this: getting the right thing across to different personalities so the context survives, the goal stays visible, and everyone knows what to do.
Working with agents, it's the same skill. The model isn't the bottleneck — being able to say what you want and what "good" looks like, to something that doesn't live in your head, is.
@emollick my takeaways General intermediate understanding of everything is good enough to get good output from AI . Specialists are still needed at the tail end for verification.
domain expertise > technical skills
@emollick Is the headline not how surprisingly even the results are? Management is just a few % points above law and that if you don't include judged success
@emollick It makes sense because they understand the full workflow. Might be worth checking middle management numbers to see if AI is already shifting people from lower roles into management
@emollick Living proof here. Couldn't write a line of code before 2025. What carries me isn't syntax, it's saying exactly what I want and what 'good' looks like. The build became a management job.
@emollick Knowing exactly what good output looks like was always the actual skill.
The code was never the hard part for the people who managed it well.
@emollick vibe manager
Spot on. Management is the real superpower here — not just prompting, but defining what ‘good’ and ‘safe’ actually look like. In my setups the agent can plan and read autonomously, but any write/deploy/payment action is scoped to a separate process with a human gate. An LLM will get socially engineered eventually. Curious how you’re thinking about the accountability layer as agents get more autonomous?
@emollick I personally know such people who already use it, and this is really a power.
@emollick management was always the skill
the tools just made it visible
@emollick ngl PMs who write 10-page PRDs are about to out-ship senior devs
@emollick this tracks. managing an agent is just managing a person with no ego :) scope it, define done, catch the drift early. if you can't delegate to humans you won't delegate to agents. the bottleneck was never the model.
This makes perfect sense. The shift from writing code to steering an agent means software development is becoming much more about task delegation, clear acceptance criteria, and system architecture. Managers spend all day refining objectives and setting expectations, which is exactly how you get the best performance out of tools like Claude Code.
It turns out that treating an AI agent like a highly capable, context-aware direct report is the ultimate productivity hack.
#ClaudeAI #SoftwareEngineering
@emollick management was always the skill
@emollick The tell is how someone delegates to people. Managers who hand a human a vague ask and hope for the best do the same to an agent, then blame the model. The ones who win write a real spec, a definition of done, and two examples of what bad output looks like. Same muscle, new tool.
@emollick What else should be used?
@emollick Management is indeed the unsung AI skill, clear direction beats raw coding talent every time.
@emollick "Compute is free" - my API bill disagrees. The bitter lesson was about scaling vs hand-engineering, not burning tokens on redundant eval. Dov is right that iteration is refinement, not waste.